Federal agencies are encountering an expanding array of sky services, options, and offerings as cloud computing environments continue to grow and evolve. It is imperative for agencies to formulate well-informed strategies to comprehend, predict, rationalize, and optimize crucial cloud architecture decisions. This approach aims to reduce operational costs and streamline application interactions effectively.
Vice President of sky systems at Octo, Sandeep Shilawat.
Understanding the fundamentals of sky architecture is essential for strategic development. The four primary categories of cloud computing architectures include personal, public, cross, and multi-cloud. Multi-cloud configurations can involve various public cloud service providers (CSPs), while hybrid clouds combine on-site data centers with public cloud services. Each architecture type impacts organizations differently.
AI and sky technology convergence
The integration of AI and cloud technology has been evident since the inception of these technologies. While cloud computing initially facilitated the advancement of AI, the focus has shifted towards leveraging AI for cybersecurity and cloud management in recent years. AI plays a pivotal role in developing AI-driven assistants for tasks such as security and operational enhancement in the realm of AIOPs (AI for Operations).
Utilizing AI to tackle national market challenges in cloud modernization
AI and conceptual AI can assist decision-makers in navigating complex cloud modernization processes and accelerating the elimination of technical debt. These technologies can address key obstacles in this domain.
First obstacle: Legacy software
Legacy applications pose a challenge in the national market as the default trend has shifted towards cloud adoption in recent years. AI can expedite the migration of legacy applications to support organizational objectives effectively. Conceptual AI shows promise in transitioning on-premises infrastructures to public or private clouds.
The primary challenge with legacy programs lies in the discovery and assessment process within complex infrastructures. Deep learning tools can navigate legacy environments, gather data for comprehensive application assessments, and provide insights for modernization roadmaps. Conceptual AI, through interactive methods, can leverage deep learning datasets to facilitate informed decision-making in development processes.
Challenge 2: Decision-making
Selecting the optimal course of action for cloud modernization is a critical issue. Linear decisions between development and migration may not always be the most beneficial for firms or agencies. Leveraging automated program rationalization frameworks and relational AI techniques can aid in making informed migration decisions at a portfolio level. A conceptual AI-based query system can enhance decision-making processes for desired outcomes.
Third Challenge: Cost Management
The flexibility of the cloud can lead to “sky sprawl” over time, resulting in increased costs and technical debt. Implementing AI and relational AI tools for cloud cost management can help businesses save significantly and ensure compliance with federal regulations. By optimizing infrastructure through regular cost recommendations and budget planning, organizations can mitigate the impact of migration costs effectively.
Compliance as the fourth challenge
Ensuring cloud compliance is paramount in national sectors where regulatory and security measures are of utmost importance. Generative AI tools can establish a foundation for secure and best-practice cloud environments using template-based infrastructure-as-code. Conceptual AI tools can independently assess threats, compliance with corporate policies, adherence to industry standards, and alignment with cloud provider frameworks like the AWS Well-Architected Framework. Proactively identifying misconfigurations is crucial for addressing a significant portion of cloud security issues.
Beyond conventional management
Managing a cloud system comprising numerous resources solely through traditional tools like widgets and spreadsheets can be daunting and risky. AI-driven solutions, particularly in the realm of AIOPs, offer advanced capabilities for managing cross and multi-cloud environments. These include AI-based load management techniques, FinOps strategies for financial operations, data management rules, workload-specific security measures, and employee training initiatives.
In conclusion, governmental agencies seeking to reduce technical debt and expedite mission-critical cloud modernization initiatives can derive substantial benefits from AI and generative AI technologies. These capabilities span the entire spectrum from decision-making to operational efficiency. While the market for AI solutions in cloud technology continues to evolve, many independent software vendors (ISVs) and service providers are already offering innovative solutions in this space.
IBM Consulting is poised to integrate conceptual AI solutions into its exclusive IBM Consultant Cloud Accelerator, catering to clients aiming to enhance their AWS infrastructure and accelerate their cloud transformation journey.
